Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations
Year: 2025
Type: article
Abstract: The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.
Source: Genome biology
Institutions: University of Oslo, University of California, Los Angeles
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funder: Universitetet i Oslo
OpenAlex ID: W4413293989